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- Bengtsson, Calle, 1934, et al.
(författare)
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A framework for quantifying net benefits of alternative prognostic models
- 2012
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Ingår i: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 31:2, s. 114-130
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Tidskriftsartikel (refereegranskat)abstract
- New prognostic models are traditionally evaluated using measures of discrimination and risk reclassification, but these do not take full account of the clinical and health economic context.We propose a framework for comparing prognostic models by quantifying the public health impact (net benefit) of the treatment decisions they support, assuming a set of predetermined clinical treatment guidelines. The change in net benefit is more clinically interpretable than changes in traditional measures and can be used in full health economic evaluations of prognostic models used for screening and allocating risk reduction interventions.We extend previous work in this area by quantifying net benefits in life years, thus linking prognostic performance to health economic measures; by taking full account of the occurrence of events over time; and by considering estimation and cross-validation in a multiple-study setting. The method is illustrated in the context of cardiovascular disease risk prediction using an individual participant data meta-analysis. We estimate the number of cardiovascular-disease-free life years gained when statin treatment is allocated based on a risk prediction model with five established risk factors instead of a model with just age, gender and region. We explore methodological issues associated with themultistudy design and show that cost-effectiveness comparisons based on the proposed methodology are robustagainst a range of modelling assumptions, including adjusting for competing risks.
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- Burgess, S., et al.
(författare)
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Bayesian methods for meta-analysis of causal relationships estimated using genetic instrumental variables
- 2010
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Ingår i: Statistics in medicine. - : Wiley. - 1097-0258 .- 0277-6715. ; 29:12, s. 1298-311
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Tidskriftsartikel (refereegranskat)abstract
- Genetic markers can be used as instrumental variables, in an analogous way to randomization in a clinical trial, to estimate the causal relationship between a phenotype and an outcome variable. Our purpose is to extend the existing methods for such Mendelian randomization studies to the context of multiple genetic markers measured in multiple studies, based on the analysis of individual participant data. First, for a single genetic marker in one study, we show that the usual ratio of coefficients approach can be reformulated as a regression with heterogeneous error in the explanatory variable. This can be implemented using a Bayesian approach, which is next extended to include multiple genetic markers. We then propose a hierarchical model for undertaking a meta-analysis of multiple studies, in which it is not necessary that the same genetic markers are measured in each study. This provides an overall estimate of the causal relationship between the phenotype and the outcome, and an assessment of its heterogeneity across studies. As an example, we estimate the causal relationship of blood concentrations of C-reactive protein on fibrinogen levels using data from 11 studies. These methods provide a flexible framework for efficient estimation of causal relationships derived from multiple studies. Issues discussed include weak instrument bias, analysis of binary outcome data such as disease risk, missing genetic data, and the use of haplotypes.
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- Burman, C. F., et al.
(författare)
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The dual test: Safeguarding p-value combination tests for adaptive designs
- 2010
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Ingår i: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 29:7-8, s. 797-807
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Tidskriftsartikel (refereegranskat)abstract
- Many modern adaptive designs apply an analysis where p-values from different stages are weighted together to an overall hypothesis test. One merit of this combination approach is that the design can be made very flexible. However, combination tests violate the sufficiency and conditionality principles. As a consequence, combination tests may lead to absurd conclusions, such as 'proving' a positive effect while the average effect is negative. We explore the possibility of modifying the test so that such illogical conclusions are no longer possible. The dual test requires both the weighted combination test and a nave test, ignoring the adaptations, to be statistically significant. The result is that the flexibility and type I error level control of the combination test are preserved, while the nave test adds a safeguard against unconvincing results. The dual test is, by construction, at least as conservative as the combination test. However, many design changes will not lead to any power loss. A typical situation where the combination approach can be used is two-stage sample size reestimation (SSR). For this case, we give a complete specification of all sample size modifications for which the two tests are equally powerful. We also study the overall power loss for some suggested SSR rules. Rules based on conditional power generally lead to ignorable power loss while a decision analytic approach exhibits clear discrepancies between the two tests.
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